Python for Finance

Preface

Not too long ago, Python as a programming language and platform technology was considered exotic—if not completely irrelevant—in the financial industry. By contrast, in 2014 there are many examples of large financial institutions—like Bank of America Merrill Lynch with its Quartz project, or JP Morgan Chase with the Athena project—that strategically use Python alongside other established technologies to build, enhance, and maintain some of their core IT systems. There is also a multitude of larger and smaller hedge funds that make heavy use of Python’s capabilities when it comes to efficient financial application development and productive financial analytics efforts.

Similarly, many of today’s Master of Financial Engineering programs (or programs awarding similar degrees) use Python as one of the core languages for teaching the translation of quantitative finance theory into executable computer code. Educational programs and trainings targeted to finance professionals are also increasingly incorporating Python into their curricula. Some now teach it as the main implementation language.

There are many reasons why Python has had such recent success and why it seems it will continue to do so in the future. Among these reasons are its syntax, the ecosystem of scientific and data analytics libraries available to developers using Python, its ease of integration with almost any other technology, and its status as open source. (See Chapter 1 for a few more insights in this regard.) ...